Google search patterns reveal early signs of stock market movements

25 April 2013

Patterns in Google searches for financial terms could
have been used as early warning signs of stock market movements, according to a
new study published in Scientific Reports.

Researchers from UCL Civil, Environmental and Geomatic
Engineering, Warwick Business School
and the Department of Physics at Boston
University highlight the
insights large data sets can give into collective decision-making. While stock
market data provide detailed records of the actions that drive market
movements, they say little about how traders decide to take these actions.

Tobias Preis, Helen Susannah Moat and H. Eugene Stanley looked for
relationships between records of searches on Google between 2004 and 2011 and
movements of the Dow Jones Industrial Average. Their analysis of seven years of
historical data detected increases in searches for financial market related
keywords before stock market falls.

The researchers identified a set of 98 terms, such as “debt”, “stocks”,
“derivatives” and “finance”, and investigated the behaviour of a simple
hypothetical trading strategy based on how the frequency of searches for these
terms changed weekly between 2004 and 2011.

We were intrigued by the idea that data from usage of online search engines might reflect how humans gather information before making decisions.

Dr Helen Susannah Moat

If the number of searches for a term had decreased in the previous week,
compared to earlier weeks, they took a long position on the Dow Jones
Industrial Average, buying stock at the start of the week and selling it at the
end, benefiting if the price had increased. If, on the other hand, the search
volume for a given term had increased then stock was sold at the beginning of
the week and bought back at the end, making a profit if the price had dropped.

Performance of this simple Google
Trends-based investment strategy varied depending on the term chosen. To investigate
this variation, Preis, Moat and Stanley created an index of ‘financial
relevance’ by calculating how often each term occurred in the Financial Times
between August 2004 to June 2011 and adjusting this count for frequency of
usage in more general language, estimated using the number of Google hits for a
term. Analysis showed that more financially relevant terms tended to lead to
higher trading strategy returns.

The results of the study are in
line with the suggestion that investors may expend more efforts searching for
information about the market before they are prepared to sell at lower prices.

Dr Helen Susannah Moat (UCL Civil, Environmental and Geomatic
Engineering) said, “By combining data created through our everyday internet
usage with large data sets capturing decisions that we make in the real world, we
have an opportunity to investigate how information flow affects our behaviour
at an unprecedented scale.”

The
study was carried out as part of the IARPA Open Source Indicators programme,
which aims to develop methods for continuous, automated analysis of publicly
available data in order to anticipate significant societal events.

Dr Tobias Preis (Warwick
Business School)
said, “Our results help illustrate the possibilities that large social data
sets are creating for a new interdisciplinary ‘computational social science’.”

“This work illustrates the insight that publicly available data can provide
to identify early warning signals of emerging events in the world,” said Jason
Matheny, programme manager of the Open Source Indicators programme at IARPA.

Image: The closing prices of the Dow Jones Industrial Average (DJIA) are shown
here as a black line, plotted against a colour code representing how the
number of searches by US users for the term ‘debt’ changes over time.
Red shows an increase in searches, while blue shows a decrease.